Probing the dynamics and bottleneck of the key atmospheric SO2 oxidation reaction by the hydroxyl radical

SO2 (Sulfur dioxide) is the major precursor to the production of sulfuric acid (H2SO4), contributing to acid rain and atmospheric aerosols. Sulfuric acid formed from SO2 generates light-reflecting sulfate aerosol particles in the atmosphere. This property has prompted recent geoengineering proposals to inject sulfuric acid or its precursors into the Earth's atmosphere to increase the planetary albedo to counteract global warming. SO2 oxidation in the atmosphere by the hydroxyl radical HO to form HOSO2 is a key rate-limiting step in the mechanism for forming acid rain. However, the dynamics of the HO + SO2 → HOSO2 reaction and its slow rate in the atmosphere are poorly understood to date. Herein, we use photoelectron spectroscopy of cryogenically cooled HOSO2- anion to access the neutral HOSO2 radical near the transition state of the HO + SO2 reaction. Spectroscopic and dynamic calculations are conducted on the first ab initio-based full-dimensional potential energy surface to interpret the photoelectron spectra of HOSO2- and to probe the dynamics of the HO + SO2 reaction. In addition to the finding of a unique pre-reaction complex (HO⋯SO2) directly connected to the transition state, dynamic calculations reveal that the accessible phase space for the HO + SO2 → HOSO2 reaction is extremely narrow, forming a key reaction bottleneck and slowing the reaction rate in the atmosphere, despite the low reaction barrier. This study underlines the importance of understanding the full multidimensional potential energy surface to elucidate the dynamics of complex bimolecular reactions involving polyatomic reactants.

Development of surface observation-based two-step emissions adjustment and its application on CO, NOx, and SO2 emissions in China and South Korea

It is challenging to estimate local emission conditions of a downwind area solely based on concentrations in the downwind area. This is because air pollutants that have a long residence time in the atmosphere can be transported over long distances and influence air quality in downwind areas. In this study, a Two-step Emissions Adjustment (TEA) approach was developed to adjust downwind emissions of target air pollutants with surface observations, considering their long-range transported emission impacts from upwind areas calculated from air quality simulations. Using the TEA approach, CO, NOx, and SO2 emissions were adjusted in China and South Korea between 2016 and 2021 based on existing bottom-up emissions inventories. Simulations with the adjusted emissions showed that the 6-year average normalized mean biases of the monthly mean concentrations of CO, NOx, and SO2 improved to 0.3 %, -2 %, and 2 %, respectively, in China, and to 5 %, 7 %, and 4 %, respectively, in South Korea. When analyzing the emission trends, it was estimated that the annual emissions of CO, NOx, and SO2 in China decreased at a rate of 7.2 %, 4.5 %, and 10.6 % per year, respectively. The decrease rate of emissions for each of these pollutants was similar to that of ambient concentrations. When considering upwind emission impacts in the emissions adjustment, CO emissions increased by 1.3 %/year in South Korea, despite CO concentrations in the country decreasing during the study period. During the study period, NOx and SO2 emissions in South Korea decreased by 3.9 % and 0.5 %/year, respectively. Moreover, the TEA approach can account for drastic short-term emission changes (e.g., social distancing due to COVID-19). Therefore, the TEA approach can be used to adjust emissions and improve reproducibility of concentrations of air pollutants suitable for health studies for areas where upwind emission impacts are significant.

Impact of Covid-19 lockdown regulations on PM2.5 and trace gases (NO2, SO2, CH4, HCHO, C2H2O2 and O3) over Lahore, Pakistan

The COVID-19 pandemic altered the human mobility and economic activities immensely, as authorities enforced unprecedented lock down regulations. In order to reduce the spread of COVID-19, a complete lockdown was observed between 24 March - 31 May 2020 in Pakistan. This paper aims at investigating the PM_2.5, AOD and column amounts of six trace gases (NO₂, SO₂, CH₄, HCHO, C₂H₂O₂, and O₃) by comparing periods of reduced emissions during lockdown periods with reference periods without emission reductions over Lahore, Pakistan. HYSPLIT cluster trajectory analyses were performed, which confirmed similar meteorological flow conditions during lockdown and reference periods. This provides confidence that any change in air quality conditions would be due to changes in human activities and associated emissions. The results show about 38% reduction in ambient surface PM_2.5 levels during the lockdown period. This change also positively correlated with MODIS_DB and AERONET_AOD data with a decrease of AOD by 42% and 35%, respectively. Reductions for tropospheric columns of NO₂ and SO₂ were about 20% and 50%, respectively during a semi lockdown period, while no reduction in the CH_4, C₂H₂O₂, HCHO and O₃ levels occurred. During the lockdown period NO_2, O₃ and CH₄ were about 50%, 45% and 25% lower, respectively, but no reduction in SO₂, C₂H₂O₂ and HCHO levels were noticed compared to the reference lockdown period for Lahore. HYSPLIT cluster trajectory analysis revealed the greatest impact on Lahore air quality through local emissions and regional transport from the east (agricultural burning and industry).

A review of Space-Air-Ground integrated remote sensing techniques for atmospheric monitoring

Currently, the three-dimensional (3D) distribution and characteristics of air pollution cannot be understood based on the application of any single atmospheric monitoring technology. Long-term, high-precision and large-scale 3D atmospheric monitoring might become practical by combining heterogeneous modern technologies; for this purpose, the Space-Air-Ground integrated system is a promising concept. In this system, optical remote sensing technologies employing fixed or mobile platforms are used as the main means for ground-based observations. Tethered balloons, unmanned aerial vehicles (UAV) and airborne platforms serve as the air-based observation segment. The final part, satellite remote sensing, corresponds to space-based observations. Aside from obtaining the 3D distribution of air pollution, research on emission estimation and pollution mechanisms has been extensively implemented based on the strengths of this system or some portion of it. Moreover, further research on the fusion of multi-source data, optimization of inversion algorithms, and coupling with atmospheric models is of great importance to the realization of this system.

Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining

This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO₂), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO₂ concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO₂ concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO₂ concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO₂ concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported "sustainable" companies had a weaker effect on local SO₂ concentrations compared to their "non-sustainable" counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology.

A data-augmentation approach to deriving long-term surface SO2 across Northern China: Implications for interpretable machine learning

Until recently, Northern China was one of the most SO₂ polluted regions in the world. The lack of long-term and spatially resolved surface SO₂ data hinders retrospective evaluation of relevant environmental policies and human health effects. This study aims to derive the spatiotemporal distribution of surface SO₂ across Northern China during 2005-2019. As "concept drift" causes substantial estimation bias in back-extrapolation, we propose a new approach named the robust back-extrapolation via data augmentation approach (RBE-DA) to model the long-term surface SO₂. The results show that the population-weighted regional SO₂ ([SO₂]_pw) increased from 2005 to 2007 and decreased steadily afterwards. The [SO₂]_pw decreased by 80.4% from 74.2 ± 28.4 μg/m³ in 2007 to 14.6 ± 4.8 μg/m³ in 2019. The predicted spatial distributions for each year show that the SO₂ pollution was severe (more than 20 μg/m³) in most areas of Northern China until 2017. By using model interpretation methods, we visually reveal the mechanism of estimation bias in the back-extrapolation. Specifically, the training data is severely imbalanced with respect to the satellite-retrieved SO₂ column densities (i.e., it is short on high-value samples), so the benchmark model is unable to extrapolate the effects of this important predictor. This study provides long-term surface SO₂ data for post hoc evaluation and human exposure assessment in Northern China, while demonstrating that the interpretable machine learning approach is critical for model diagnostics and refinement. Leveraging satellite retrievals, the RBE-DA approach can be applied worldwide to back-extrapolate various measures of air quality.

Hydrogen-Bond Topology Is More Important Than Acid/Base Strength in Atmospheric Prenucleation Clusters

We explored the hypothesis that on the nanoscale level, acids and bases might exhibit different behavior than in bulk solution. Our study system consisted of sulfuric acid, formic acid, ammonia, and water. We calculated highly accurate Domain-based Local pair-Natural Orbital- Coupled-Cluster/Complete Basis Set (DLPNO-CCSD(T)/CBS) energies on DFT geometries and used the resulting Gibbs free energies for cluster formation to compute the overall equilibrium constants for every possible cluster. The equilibrium constants combined with the initial monomer concentrations were used to predict the formation of clusters at the top and the bottom of the troposphere. Our results show that formic acid is as effective as ammonia at forming clusters with sulfuric acid and water. The structure of formic acid is uniquely suited to form hydrogen bonds with sulfuric acid. Additionally, it can partner with water to form bridges from one side of sulfuric acid to the other, hence demonstrating that hydrogen bonding topology is more important than acid/base strength in these atmospheric prenucleation clusters.

Sector-Based Top-Down Estimates of NO x , SO2, and CO Emissions in East Asia

Top-down estimates using satellite data provide important information on the sources of air pollutants. We develop a sector-based 4D-Var framework based on the GEOS-Chem adjoint model to address the impacts of co-emissions and chemical interactions on top-down emission estimates. We apply OMI NO₂, OMI SO₂, and MOPITT CO observations to estimate NO _x , SO₂, and CO emissions in East Asia during 2005-2012. Posterior evaluations with surface measurements show reduced normalized mean bias (NMB) by 7% (NO₂)-15% (SO₂) and normalized mean square error (NMSE) by 8% (SO₂)-9% (NO₂) compared to a species-based inversion. This new inversion captures the peak years of Chinese SO₂ (2007) and NO _x (2011) emissions and attributes their drivers to industry and energy activities. The CO peak in 2007 in China is driven by residential and industry emissions. In India, the inversion attributes NO _x and SO₂ trends mostly to energy and CO trend to residential emissions.

Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation

Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO2) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO2 concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO2 emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO2 forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO2 emission optimization methodology is computationally cost-effective.

Analysis of atmospheric SO2 in Sichuan-Chongqing region based on OMI data

The Sichuan-Chongqing region is the leader and growth pole of economic development in western China. With the rapid development of economy and unique geographical environment, high concentration of sulfur dioxide air pollution has existed for a long time in Sichuan-Chongqing area. Based on 10 years of remote sensing data, this paper studies the temporal and spatial distribution characteristics, stability, and influencing factors of sulfur dioxide in this area. Based on potential sources, the impact of surrounding areas on sulfur dioxide in Sichuan and Chongqing is analyzed. The results shows that the spatial distribution of sulfur dioxide in the Sichuan-Chongqing region is higher in the southeast and lower in the west. The Midwest region has low fluctuation and good stability. The time distribution shows obvious seasonal regularity. The concentration of sulfur dioxide is affected by socio-economic factors and natural factors. In this study, it is found that the distribution of sulfur dioxide is closely related to PM_2.5, which provides an important reference for the comprehensive management of air pollution. The OMI data effectively reflects the distribution and change of atmospheric sulfur dioxide in the Sichuan-Chongqing region, and provides certain ideas for air pollution control in the Sichuan-Chongqing region and other regions in China.

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

Predicting Spatial Variations in Multiple Measures of Oxidative Burden for Outdoor Fine Particulate Air Pollution across Canada

Fine particulate air pollution (PM_2.5) is a leading contributor to the overall global burden of disease. Traditionally, outdoor PM_2.5 has been characterized using mass concentrations which treat all particles as equally harmful. Oxidative potential (OP) (per μg) and oxidative burden (OB) (per m³) are complementary metrics that estimate the ability of PM_2.5 to cause oxidative stress, which is an important mechanism in air pollution health effects. Here, we provide the first national estimates of spatial variations in multiple measures (glutathione, ascorbate, and dithiothreitol depletion) of annual median outdoor PM_2.5 OB across Canada. To do this, we combined a large database of ground-level OB measurements collected monthly prospectively across Canada for 2 years (2016-2018) with PM_2.5 components estimated using a chemical transport model (GEOS-Chem) and satellite aerosol observations. Our predicted ground-level OB values of all three methods were consistent with ground-level observations (cross-validation R² = 0.63-0.74). We found that forested regions and urban areas had the highest OB, predicted primarily by black carbon and organic carbon from wildfires and transportation sources. Importantly, the dominant components associated with OB were different than those contributing to PM_2.5 mass concentrations (secondary inorganic aerosol); thus, OB metrics may better indicate harmful components and sources on health than the bulk PM_2.5 mass, reinforcing that OB estimates can complement the existing PM_2.5 data in future national-level epidemiological studies.

Atmospheric Age Distribution of Primary and Secondary Inorganic Aerosols in a Polluted Atmosphere

The community multiscale air quality (CMAQ) model was modified to track the evolution of the atmospheric age (τ) distribution of primary particulate matter (PPM) and secondary inorganic aerosol components (nitrate, sulfate, and ammonium ion, NSA). The modified CMAQ gas and aerosol mechanisms represent the same species emitted at different times as an age-resolved mixture, using multiple age-tagged variables and a dynamic age-bin advancing scheme. The model was applied to study the spatial and temporal evolution of τ for PPM and NSA in January 2013 to understand the formation and regional transport of PM and the precursor gases during severe winter pollution episodes in China. The results showed that increases in PPM and NSA concentrations during high pollution periods in polluted urban areas were typically associated with increases in the mean atmospheric age (τ̅) due to the accumulation of local emissions and regional transport of aged pollutants. Some of the rapid sulfate growth events at the beginning of multiday air pollution episodes were driven by regional transport of aged particles. In heavily polluted cities, while most of the monthly average PPM had τ less than 10 h, more than half of the sulfate had τ greater than 20-30 h. Regional distributions showed that very aged sulfate particles with τ > 96 h accounted for a significant portion of the total sulfate and had a very broad spatial distribution. However, aged ammonium ions had very low concentrations. Aged nitrate also had lower concentrations and more limited spatial distributions than sulfate due to differences in the atmospheric lifetime between SO₂ and NO_x. The estimated NO_x lifetime of approximately ∼24 h in China agrees with a satellite-based estimation of 21 h. Potential applications of the age distribution analysis include evaluating the impacts of meteorology on air quality and developing short-term emission control strategies.

Vertical distributions of tropospheric SO2 based on MAX-DOAS observations: Investigating the impacts of regional transport at different heights in the boundary layer

Information on the vertical distribution of air pollutants is essential for understanding their spatiotemporal evolution underlying urban atmospheric environment. This paper presents the SO₂ profiles based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements from March 2018 to February 2019 in Hefei, East China. SO₂ decrease rapidly with increasing heights in the warm season, while lifted layers were observed in the cold season, indicating accumulation or long-range transport of SO₂ in different seasons might occur at different heights. The diurnal variations of SO₂ were roughly consistent for all four seasons, exhibiting the minimum at noon and higher values in the morning and late afternoon. Lifted layers of SO₂ were observed in the morning for fall and winter, implying the accumulation or transport of SO₂ in the morning mainly occurred at the top of the boundary layer. The bivariate polar plots showed that weighted SO₂ concentrations in the lower altitude were weakly dependent on wind, but in the middle and upper altitudes, higher weighted SO₂ concentrations were observed under conditions of middle-high wind speed. Concentration weighted trajectory (CWT) analysis suggested that potential sources of SO₂ in spring and summer were local and transported mainly occurred in the lower altitude from southern and eastern areas; while in fall and winter, SO₂ concentrations were deeply affected by long-range transport from northwestern and northern polluted regions in the middle and upper altitudes. Our findings provide new insight into the impacts of regional transport at different heights in the boundary layer on SO₂ pollution.

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space-based and in-situ observations. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide hindcasts and 5-days forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community. Evaluation of GEOS-CF against satellite, ozonesonde and surface observations for years 2018-2019 show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to 0.3, normalized root mean square errors between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO2 and an overprediction of summertime O3 over the Southeast United States. GEOS-CF v1.0 generally overestimates aerosols by 20%-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions. The 5-days forecasts have skill scores comparable to the 1-day hindcast. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

Impact of the COVID-19 outbreak on air pollution levels in East Asia

This study leverages satellite remote sensing to investigate the impact of the coronavirus outbreak and the resulting lockdown of public venues on air pollution levels in East Asia. We analyze data from the Sentinel-5P and the Himawari-8 satellites to examine concentrations of NO₂, HCHO, SO₂, and CO, and the aerosol optical depth (AOD) over the BTH, Wuhan, Seoul, and Tokyo regions in February 2019 and February 2020. Results show that most of the concentrations of pollutants are lower than those of February 2019. Compared to other pollutants, NO₂ experienced the most significant reductions by almost 54%, 83%, 33%, and 19% decrease in BTH, Wuhan, Seoul, and Tokyo, respectively. The greatest reductions in pollutants occurred in Wuhan, with a decrease of almost 83%, 11%, 71%, and 4% in the column densities of NO₂, HCHO, SO₂, and CO, respectively, and a decrease of about 62% in the AOD. Although NO₂, CO, and formaldehyde concentrations decreased in the Seoul and Tokyo metropolitan areas compared to the previous year, concentrations of SO₂ showed an increase in these two regions due to the effect of transport from polluted upwind regions. We also show that meteorological factors were not the main reason for the dramatic reductions of pollutants in the atmosphere. Moreover, an investigation of the HCHO/NO₂ ratio shows that in many regions of East China, particularly in Wuhan, ozone production in February 2020 is less NO_X saturated during the daytime than it was in February 2019. With large reductions in the concentrations of NO₂ during lockdown situations, we find that significant increases in surface ozone in East China from February 2019 to February 2020 are likely the result of less reaction of NO and O₃ caused by significantly reduced NO_X concentrations and less NO_X saturation in East China during the daytime.

Variability in Observation-based Onroad Emission Constraints from a Near-road Environment

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (ΔCO:ΔNOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx emission adjustments for a priori emissions estimates from EPA's MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ΔCO:ΔNOx from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115m and 300m) increases OLS and orthogonal regression estimates of ΔCO:ΔNOx but not cross-road gradient ΔCO:ΔNOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ΔCO:ΔNOx estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

Global Importance of Hydroxymethanesulfonate in Ambient Particulate Matter: Implications for Air Quality

Sulfur compounds are an important constituent of particulate matter, with impacts on climate and public health. While most sulfur observed in particulate matter has been assumed to be sulfate, laboratory experiments reveal that hydroxymethanesulfonate (HMS), an adduct formed by aqueous phase chemical reaction of dissolved HCHO and SO2, may be easily misinterpreted in measurements as sulfate. Here we present observational and modeling evidence for a ubiquitous global presence of HMS. We find that filter samples collected in Shijiazhuang, China, and examined with ion chromatography within 9 days show as much as 7.6 μg m-3 of HMS, while samples from Singapore examined 9-18 months after collection reveal ~0.6 μg m-3 of HMS. The Shijiazhuang samples show only minor traces of HMS 4 months later, suggesting that HMS had decomposed over time during sample storage. In contrast, the Singapore samples do not clearly show a decline in HMS concentration over 2 months of monitoring. Measurements from over 150 sites, primarily derived from the IMPROVE network across the United States, suggest the ubiquitous presence of HMS in at least trace amounts as much as 60 days after collection. The degree of possible HMS decomposition in the IMPROVE observations is unknown. Using the GEOS-Chem chemical transport model, we estimate that HMS may account for 10% of global particulate sulfur in continental surface air and over 25% in many polluted regions. Our results suggest that reducing emissions of HCHO and other volatile organic compounds may have a co-benefit of decreasing particulate sulfur.

Effects of the socio-economic influencing factors on SO2 pollution in Chinese cities: A spatial econometric analysis based on satellite observed data

The research on SO₂ pollution in China has been hotly debated over the past decades. Different from the existing studies, this work employs satellite observed SO₂ columns from 2005 to 2016 and applies a spatial econometric approach to investigate the socio-economic influencing factors of SO₂ pollution of 270 prefecture-level cities in China. The findings are as follows. (1) SO₂ pollution over China exhibits a significant and positive spatial autocorrelation. (2) The most polluted area is concentrated on the North China Plain. However, SO₂ pollution over China has been reduced gradually during the sample period, implying that overall environmental quality in China has been substantially improved. (3) Besides, the results of spatial econometric models are not in support of "pollution haven hypothesis". On the contrary, the pollution halo effect of foreign direct investment works well and contributes to reducing SO₂ pollution in China. Moreover, we find that urban economic levels and innovative capability are negatively correlated with SO₂ pollution, indicating that economic growth and an increase in innovation can help improve environmental quality. On contrast, the share of the secondary industry, urbanization and transportation are found to have positive impacts, indicating that they are three main contributors to SO₂ pollution in China.

Unprecedented Temporary Reduction in Global Air Pollution Associated with COVID-19 Forced Confinement: A Continental and City Scale Analysis

Shortly after the outbreak of the novel infectious disease (COVID-19) started at the end of 2019, it turned into a global pandemic, which caused the lockdown of many countries across the world. Various strict measures were adopted to reduce anthropogenic activities in order to prevent further spread and infection of the disease. In this study, we utilized continental scale remotely sensed data along with city scale in situ air quality observations for 2020 as well as data from the baseline period (2015–2019) to provide an early insight on air pollution changes in response to the COVID-19 pandemic lockdown, by combining both continental and city scales. For the continental scale analysis, data of NO2, SO2, and O3 were acquired from the ozone monitoring instrument (OMI) and data of aerosol optical depth (AOD) were collected from the moderate resolution imaging spectroradiometer (MODIS). For city scale analysis, data of NO2, CO, PM2.5, O3, and SO2 were derived from ground-based air quality observations. Results from satellite observations at the continental scale showed that concentrations of NO2, SO2, and AOD substantially dropped in 2020 during the lockdown period compared to their averages for the baseline period over all continents, with a maximum reduction of ~33% for NO2 in East Asia, ~41% for SO2 in East Asia, and ~37% for AOD in South Asia. In the case of O3, the maximum overall reduction was observed as ~11% in Europe, followed by ~10% in North America, while a slight increase was found in other study regions. These findings align with ground-based air quality observations, which showed that pollutants such as NO2, CO, PM2.5, and SO2 during the 2020 lockdown period decreased significantly except that O3 had varying patterns in different cities. Specifically, a maximum reduction of ~49% in NO2 was found in London, ~43% in CO in Wuhan, ~38% in PM2.5 in Chennai, and ~48% in SO2 in Beijing. In the case of urban O3, a maximum reduction of ~43% was found in Wuhan, but a significant increase of ~47% was observed in Chennai. It is obvious that restricted human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality, especially across the metropolitan cities.

Spatiotemporal distributions of ambient SO2 across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013-2016

Multiyear spatiotemporal distributions of daily ambient sulfur dioxide (SO₂) are essential for evaluating management effectiveness and assessing human health risk. In this study, we estimate the daily SO₂ levels across China on 0.1^o grid from 2013 to 2016 by assimilating satellite- and ground-based SO₂ observations using the random-forest spatiotemporal kriging (RF-STK) model. The cross-validation R² is 0.64 and 0.81 for predicting the daily and multiyear averages, respectively. The multiyear population-weighted average of SO₂ for China is 28.1 ± 14.0 μg/m³, and the severest SO₂ pollution occurs in the northern China (45.1 ± 14.7 μg/m³). The SO₂ concentration shows a strong seasonality, i.e., highest in winter (41.6 ± 26.4 μg/m³) and lowest in summer (19.6 ± 8.3 μg/m³). During 2013-2016, the annual SO₂ decreases from 34.4 ± 18.2 to 22.7 ± 11.1 μg/m³, and the population% exposed for more than 100 nonattainment days (SO₂ > 20 μg/m³) drops from 86% to 48%. While the seasonality of SO₂ is mainly determined by the meteorological variation, the substantial decrease attributes to the reduced emissions such as from coal consumption. The effectiveness of SO₂ emission reduction varies widely in different prefectures of China. In Shandong province, the SO₂ concentration decreases by -45% while the coal consumption increases by 9%. In Shanxi province, the SO₂ concentration decreases by -15% while the coal consumption decreases by -3%. The contrasting effectiveness between these two provinces is associated with the much fewer waste gas disposal facilities in Shanxi than Shandong. Stricter regulation is required to further lower the SO₂ concentration in order to protect the public health, especially in the northern China.

Simulation of airborne trace metals in fine particulate matter over North America

Trace metal distributions are of relevance to understand sources of fine particulate matter (PM2.5), PM2.5-related health effects, and atmospheric chemistry. However, knowledge of trace metal distributions is lacking due to limited ground-based measurements and model simulations. This study develops a simulation of 12 trace metal concentrations (Si, Ca, Al, Fe, Ti, Mn, K, Mg, As, Cd, Ni and Pb) over continental North America for 2013 using the GEOS-Chem chemical transport model. Evaluation of modeled trace metal concentrations with observations indicates a spatial consistency within a factor of 2, an improvement over previous studies that were within a factor of 3-6. The spatial distribution of trace metal concentrations reflects their primary emission sources. Crustal element (Si, Ca, Al, Fe, Ti, Mn, K) concentrations are enhanced over the central US from anthropogenic fugitive dust and over the southwestern U.S. due to natural mineral dust. Heavy metal (As, Cd, Ni and Pb) concentrations are high over the eastern U.S. from industry. K is abundance in the southeast from biomass burning and high concentrations of Mg is observed along the coast from sea spray. The spatial pattern of PM2.5 mass is most strongly correlated with Pb, Ni, As and K due to their signature emission sources. Challenges remain in accurately simulating observed trace metal concentrations. Halving anthropogenic fugitive dust emissions in the 2011 National Air Toxic Assessment (NATA) inventory and doubling natural dust emissions in the default GEOS-Chem simulation was necessary to reduce biases in crustal element concentrations. A fivefold increase of anthropogenic emissions of As and Pb was necessary in the NATA inventory to reduce the national-scale bias versus observations by more than 80 %, potentially reflecting missing sources.

Source Contributions to Ambient Fine Particulate Matter for Canada

Understanding the sectoral contribution of emissions to fine particulate matter (PM_2.5) offers information for air quality management, and for investigation of association with health outcomes. This study evaluates the contribution of different emission sectors to PM_2.5 in 2013 for Canada using the GEOS-Chem chemical transport model, downscaled with satellite-based PM_2.5. Despite the low population-weighted PM_2.5 concentrations of 5.5 μg m^-3 across Canada, we find that over 70% of population-weighted PM_2.5 originates from Canadian sources followed by 30% from the contiguous United States. The three leading sectoral contributors to population-weighted PM_2.5 over Canada are wildfires with 1.0 μg m^-3 (17%), transportation with 0.96 μg m^-3 (16%), and residential combustion with 0.91 μg m^-3 (15%). The relative contribution to population-weighted PM_2.5 of different sectors varies regionally with residential combustion as the leading contributor in Central Canada (19%), while wildfires dominate over Northern Canada (59%), Atlantic Canada (34%), and Western Canada (18%). The contribution from U.S. sources is larger over Central Canada (33%) than over Western Canada (17%), Atlantic Canada (17%), and Northern Canada (<2%). Sectoral trend analysis showed that the contribution from anthropogenic sources to population-weighted PM_2.5 decreased from 7.1 μg m^-3 to 3.4 μg m^-3 over the past two decades.

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling

This study presents land-use regression (LUR) models for submicron particulate matter (PM₁) components from an urban area. Models are presented for mass concentrations of inorganic species (SO₄, NO₃, NH₄), organic aerosol (OA) factors, and total PM₁. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km² area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R² = 0.80, HOA R² = 0.67) and lowest for secondary inorganic species (SO₄ R² = 0.47, NH₄ R² = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM₁ components through a subsampling analysis.

Hybrid Mass Balance/4D-Var Joint Inversion of NO x and SO2 Emissions in East Asia

Accurate estimates of NO x and SO2 emissions are important for air quality modeling and management. To incorporate chemical interactions of the two species in emission estimates, we develop a joint hybrid inversion framework to estimate their emissions in China and India (2005-2012). Pseudo observation tests and posterior evaluation with surface measurements demonstrate that joint assimilation of SO2 and NO2 can provide more accurate constraints on emissions than single-species inversions. This occurs through synergistic change of O3 and OH concentrations, particularly in conditions where satellite retrievals of the species being optimized have large uncertainties. The percentage changes of joint posterior emissions from the single-species posterior emissions go up to 242% at grid scales, although the national average of monthly emissions, seasonality, and interannual variations are similar. In China and India, the annual budget of joint posterior SO2 emissions is lower, but joint NO x posterior emissions are higher, because NO x emissions increase to increase SO2 concentration and better match Ozone Monitoring Instrument SO2 observations in high-NO x regions. Joint SO2 posterior emissions decrease by 16.5% from 2008 to 2012, while NO x posterior emissions increase by 24.9% from 2005 to 2011 in China-trends which are consistent with the MEIC inventory. Joint NO x and SO2 posterior emissions in India increase by 15.9% and 19.2% from 2005 to 2012, smaller than the 59.9% and 76.2% growth rate using anthropogenic emissions from EDGARv4.3.2. This work shows the benefit and limitation of joint assimilation in emission estimates and provides an efficient framework to perform the inversion.

SO2 Emission Estimates Using OMI SO2 Retrievals for 2005-2017

SO2 column densities from Ozone Monitoring Instrument provide important information on emission trends and missing sources, but there are discrepancies between different retrieval products. We employ three Ozone Monitoring Instrument SO2 retrieval products (National Aeronautics and Space Administration (NASA) standard (SP), NASA prototype, and BIRA) to study the magnitude and trend of SO2 emissions. SO2 column densities from these retrievals are most consistent when viewing angles and solar zenith angles are small, suggesting more robust emission estimates in summer and at low latitudes. We then apply a hybrid 4D-Var/mass balance emission inversion to derive monthly SO2 emissions from the NASA SP and BIRA products. Compared to HTAPv2 emissions in 2010, both posterior emission estimates are lower in United States, India, and Southeast China, but show different changes of emissions in North China Plain. The discrepancies between monthly NASA and BIRA posterior emissions in 2010 are less than or equal to 17% in China and 34% in India. SO2 emissions increase from 2005 to 2016 by 35% (NASA)-48% (BIRA) in India, but decrease in China by 23% (NASA)-33% (BIRA) since 2008. Compared to in situ measurements, the posterior GEOS-Chem surface SO2 concentrations have reduced NMB in China, the United States, and India but not in South Korea in 2010. BIRA posteriors have better consistency with the annual growth rate of surface SO2 measurement in China and spatial variability of SO2 concentration in China, South Korea, and India, whereas NASA SP posteriors have better seasonality. These evaluations demonstrate the capability to recover SO2 emissions using Ozone Monitoring Instrument observations.

Chinese province-scale source apportionments for sulfate aerosol in 2005 evaluated by the tagged tracer method

Appropriate policies to improve air quality by reducing anthropogenic emissions are urgently needed. This is typified by the particulate matter (PM) problem and it is well known that one type of PM, sulfate aerosol (SO₄^2-), has a large-scale impact due to long range transport. In this study we evaluate the source-receptor relationships of SO₄^2- over East Asia for 2005, when anthropogenic sulfur dioxide (SO₂) emissions from China peaked. SO₂ emissions from China have been declining since 2005-2006, so the possible maximum impact of Chinese contributions of SO₄^2- is evaluated. This kind of information provides a foundation for policy making and the estimation of control effects. The tagged tracer method was applied to estimate the source apportionment of SO₄^2- for 31 Chinese province-scale regions. In addition, overall one-year source apportionments were evaluated to clarify the seasonal dependency. Model performance was confirmed by comparing with ground-based observations over mainland China, Taiwan, Korea, and Japan, and the model results fully satisfied the performance goal for PM. We found the following results. Shandong and Hebei provinces, which were the largest and second largest SO₂ sources in China, had the greatest impact over the whole of East Asia with apportionments of around 10-30% locally and around 5-15% in downwind receptor regions during the year. Despite large SO₂ emissions, the impact of south China (e.g., Guizhou, Guangdong, and Sichuan provinces) was limited to local impact. These results suggest that the reduction policy in south China contributes to improving the local air quality, whereas policies in north and central China are beneficial for both the whole of China and downwind regions. Over Taiwan, Korea, and Japan, the impact of China was dominant; however, local contributions were important during summer.

NOx emission trends over Chinese cities estimated from OMI observations during 2005 to 2015

Satellite NO₂ observations have been widely used to evaluate emission changes. To determine trends in NO_x emission over China, we used a method independent of chemical transport models to quantify the NO_x emissions from 48 cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO₂ observations during 2005 to 2015. We found that NO_x emissions over 48 Chinese cities increased by 52% from 2005 to 2011 and decreased by 21% from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e. power, industrial and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NO_x emissions was consistent with the bottom-up emission inventories for all power plants (r=0.8 on average), but not for some cities (r=0.4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to cities by using spatial distribution proxies.

Anthropogenic Sulfur Perturbations on Biogenic Oxidation: SO2 Additions Impact Gas-Phase OH Oxidation Products of α- and β-Pinene

In order to probe how anthropogenic pollutants can impact the atmospheric oxidation of biogenic emissions, we investigated how sulfur dioxide (SO2) perturbations impact the oxidation of two monoterpenes, α-and β-pinene. We used chemical ionization mass spectrometry to examine changes in both individual molecules and gas-phase bulk properties of oxidation products as a function of SO2 addition. SO2 perturbations impacted the oxidation systems of α-and β-pinene, leading to an ensemble of products with a lesser degree of oxygenation than unperturbed systems. These changes may be due to shifts in the OH:HO2 ratio from SO2 oxidation and/or to SO3 reacting directly with organic molecules. Van Krevelen diagrams suggest a shift from gas-phase functionalization by alcohol/peroxide groups to functionalization by carboxylic acid or carbonyl groups, consistent with a decreased OH:HO2 ratio. Increasing relative humidity dampens the impact of the perturbation. This decrease in oxygenation may impact secondary organic aerosol formation in regions dominated by biogenic emissions with nearby SO2 sources. We observed sulfur-containing organic compounds following SO2 perturbations of monoterpene oxidation; whether these are the result of photochemistry or an instrumental artifact from ion-molecule clustering remains uncertain. However, our results demonstrate that the two monoterpene isomers produce unique suites of oxidation products.

Dissolved organic and inorganic matter in bulk deposition of a coastal urban area: an integrated approach

Bulk deposition can remove atmospheric organic and inorganic pollutants that may be associated with gaseous, liquid or particulate phases. To the best of our knowledge, few studies have been carried out, which simultaneously analyse the presence of organic and inorganic fractions in rainwater. In the present work, the complementarity of organic and inorganic data was assessed, through crossing data of some organic [DOC (dissolved organic carbon), absorbance at 250 nm (UV250nm), integrated fluorescence] and inorganic [H(+), NH4(+), NO3(-), non sea salt sulphate (NSS-SO4(2-))] parameters measured in bulk deposition in the coastal urban area of Aveiro. The organic and inorganic parameters analysed were positively correlated (p<0.001) except for H(+), which suggests that a constant fraction of chromophoric dissolved organic matter (CDOM) came from anthropogenic sources. Furthermore, the inverse correlations observed for the organic and inorganic parameters with the precipitation amount suggest that organic and inorganic fractions were incorporated into the rainwater partially by below-cloud scavenging of airborne particulate matter. This is in accordance with the high values of DOC and NO3(-) found in samples associated with marine air masses, which were linked in part to the contribution of local emissions from vehicular traffic. DOC of bulk deposition was the predominant constituent when compared with the constituents H(+), NH4(+), NO3(-) and NSS-SO4(2-), and consequently bulk deposition flux was also highest for DOC, highlighting the importance of DOC and of anthropogenic ions being simultaneously removed from the atmosphere by bulk deposition. However, it was verified that the contribution of anthropogenic sources to the DOC of bulk deposition may be different for distinct urban areas. Thus, it is recommended that organic and inorganic fractions of bulk deposition are studied together.

Global chemical composition of ambient fine particulate matter for exposure assessment

Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1° × 0.1° spatial resolution for 2004-2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 μg/m(3)), secondary inorganic aerosol (11.1 ± 5.0 μg/m(3)), and mineral dust (11.1 ± 7.9 μg/m(3)). Secondary inorganic PM2.5 concentrations exceeded 30 μg/m(3) over East China. Sensitivity simulations suggested that population-weighted ambient PM2.5 from biofuel burning (11 μg/m(3)) could be almost as large as from fossil fuel combustion sources (17 μg/m(3)). These estimates offer information about global population exposure to the chemical components and sources of PM2.5.

Ozone monitoring instrument observations of interannual increases in SO2 emissions from Indian coal-fired power plants during 2005-2012

Due to the rapid growth of electricity demand and the absence of regulations, sulfur dioxide (SO2) emissions from coal-fired power plants in India have increased notably in the past decade. In this study, we present the first interannual comparison of SO2 emissions and the satellite SO2 observations from the Ozone Monitoring Instrument (OMI) for Indian coal-fired power plants during the OMI era of 2005-2012. A detailed unit-based inventory is developed for the Indian coal-fired power sector, and results show that its SO2 emissions increased dramatically by 71% during 2005-2012. Using the oversampling technique, yearly high-resolution OMI maps for the whole domain of India are created, and they reveal a continuous increase in SO2 columns over India. Power plant regions with annual SO2 emissions greater than 50 Gg year(-1) produce statistically significant OMI signals, and a high correlation (R = 0.93) is found between SO2 emissions and OMI-observed SO2 burdens. Contrary to the decreasing trend of national mean SO2 concentrations reported by the Indian Government, both the total OMI-observed SO2 and annual average SO2 concentrations in coal-fired power plant regions increased by >60% during 2005-2012, implying the air quality monitoring network needs to be optimized to reflect the true SO2 situation in India.

Measuring global volcanic degassing with the Ozone Monitoring Instrument (OMI)

The ultraviolet (UV) Ozone Monitoring Instrument (OMI), launched on NASA's Aura satellite in July 2004, was the first space-based sensor to provide operational sulphur dioxide (SO2) measurements (OMSO2) for use by the scientific community. Herein, we discuss the application of OMSO2 data for the monitoring of global volcanic SO2 emissions, with an emphasis on lower tropospheric volcanic plumes. We review the algorithms used to produce OMSO2 data and highlight some key measurement sensitivity issues. The data processing scheme used to generate web-based OMSO2 data subsets for volcanic regions and estimate SO2 burdens in volcanic plumes is outlined. We describe three techniques to derive SO2 emission rates from the OMSO2 measurements, and employ one method (using single OMI pixels to estimate SO2 fluxes) to elucidate SO2 flux detection thresholds on a global scale. Applications of OMSO2 data to volcanic degassing studies are demonstrated using four case studies. These examples show how OMSO2 measurements correlate with changes in eruptive activity at Kilauea volcano (Hawaii), constrain small, potentially significant SO2 releases from reawakening, historically inactive volcanoes, track long-term changes in SO2 degassing from Nyiragongo volcano (D.R. Congo), and detect SO2 emissions from the remote Lastarria Volcano (Chile), in the actively deforming Lazufre region.